---
type: source
title: "LLMs Systematically Bias Nursing Care Plan Content AND Expert-Rated Quality Across 96 Sociodemographic Identity Combinations (JMIR, 2025)"
author: "JMIR Research Team (first study of sociodemographic bias in LLM-generated nursing care)"
url: https://www.jmir.org/2025/1/e78132
date: 2025-01-01
domain: health
secondary_domains: [ai-alignment]
format: research paper
status: processed
processed_by: vida
processed_date: 2026-04-04
priority: medium
tags: [sociodemographic-bias, nursing-care, llm-clinical-bias, health-equity, gpt, nature-medicine-extension, belief-5, belief-2]
extraction_model: "anthropic/claude-sonnet-4.5"
claims_extracted:
- "llm-nursing-care-plans-exhibit-dual-pathway-sociodemographic-bias-in-content-and-expert-rated-quality"
---

## Content

Published in Journal of Medical Internet Research (JMIR), 2025, volume/issue 2025/1, article e78132. Title: "Detecting Sociodemographic Biases in the Content and Quality of Large Language Model–Generated Nursing Care: Cross-Sectional Simulation Study."

**Study design:**
- Cross-sectional simulation study
- Platform tested: GPT (specific version not specified in summary)
- 96 sociodemographic identity combinations tested
- 9,600 nursing care plans generated and analyzed
- Dual outcome measures: (1) thematic content of care plans, (2) expert-rated clinical quality of care plans
- Described as "first empirical evidence" of sociodemographic bias in LLM-generated nursing care

**Key findings:**
- LLMs systematically reproduce sociodemographic biases in nursing care plan **content** (what topics/themes are included)
- LLMs systematically reproduce sociodemographic biases in **expert-rated clinical quality** (nurses rating quality differ by patient demographics, holding AI output constant)
- "Reveal a substantial risk that such models may reinforce existing health inequities"

**Significance:**
- First study of this type specifically for nursing care (vs. physician emergency department decisions in Nature Medicine)
- Bias appears in BOTH the content generated AND the perceived quality — dual pathway
- This extends the Nature Medicine finding (physician emergency department decisions) to a different care setting (nursing care planning), different AI platform (GPT vs. the 9 models in Nature Medicine), and different care type (planned/scheduled vs. emergency triage)

## Agent Notes

**Why this matters:** The Nature Medicine 2025 study (9 LLMs, 1.7M outputs, emergency department physician decisions — already archived March 22) showed demographic bias in physician clinical decisions. This JMIR study independently confirms demographic bias in a completely different context: nursing care planning, using a different AI platform, a different research group, and a different care setting. Two independent studies, two care settings, two AI platforms, same finding — pervasive sociodemographic bias in LLM clinical outputs across care contexts and specialties. This strengthens the inference that OE's model (whatever it is) carries similar demographic bias patterns, since the bias has now been documented in multiple contexts.

**What surprised me:** The bias affects not just content (what topics are covered) but expert-rated clinical quality. This means that clinicians EVALUATING the care plans perceive higher or lower quality based on patient demographics — even when it's the AI generating the content. This is a confound for clinical oversight: if the quality rater is also affected by demographic bias, oversight doesn't catch the bias.

**What I expected but didn't find:** OE-specific evaluation. This remains absent across all searches. The JMIR study uses GPT; the Nature Medicine study uses 9 models (none named as OE). OE remains unevaluated.

**KB connections:**
- Extends Nature Medicine (2025) demographic bias finding from physician emergency decisions to nursing care planning — second independent study confirming LLM clinical demographic bias
- Relevant to Belief 2 (non-clinical determinants): health equity implications of AI-amplified disparities connect to SDOH and the structural diagnosis of health inequality
- Relevant to Belief 5 (clinical AI safety): the dual bias (content + quality perception) means that clinical oversight may not catch AI demographic bias because overseers share the same bias patterns

**Extraction hints:** Primary claim: LLMs systematically produce sociodemographically biased nursing care plans affecting both content and expert-rated clinical quality — the first empirical evidence for this failure mode in nursing. Confidence: proven (9,600 tests, 96 identity combinations, peer-reviewed JMIR). Secondary claim: the JMIR and Nature Medicine findings together establish a pattern of pervasive LLM sociodemographic bias across care settings, specialties, and AI platforms — making it a robust pattern rather than a context-specific artifact. Confidence: likely (two independent studies, different contexts, same directional finding; OE-specific evidence still absent).

**Context:** JMIR is a high-impact medical informatics journal. The "first empirical evidence" language in the abstract is strong — the authors claim priority for this specific finding (nursing care, dual bias). This will likely generate follow-on work and citations in clinical AI safety discussions. The study's limitation (single AI platform — GPT) is real but doesn't invalidate the finding; it just means replication with other platforms is needed.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: Nature Medicine 2025 sociodemographic bias study (already archived) — this JMIR paper is the second independent study confirming the same pattern
WHY ARCHIVED: Extends demographic bias finding to nursing settings — strengthens the inference that OE carries demographic bias by documenting the pattern's robustness across care contexts
EXTRACTION HINT: Extract as an extension of the Nature Medicine finding. The claim should note this is the second independent study confirming LLM sociodemographic bias in clinical contexts. The dual bias (content AND quality) is the novel finding beyond Nature Medicine's scope — make that the distinct claim.
